How does this skill handle high cardinality metrics?

The skill enforces architectural guards against using high-cardinality labels like user IDs or request IDs in metrics, preventing storage bloat and ensuring query performance.

Is this skill grounded in industry standards?

Absolutely. It is grounded in Google's Site Reliability Engineering (SRE) practices, SWEBOK, and modern observability engineering research.

How does it improve incident response?

By requiring every alert to be paired with a clear runbook, it ensures that when an SLI is violated, the engineering team has an immediate action plan to resolve the issue.

Does it support distributed tracing patterns?

Yes, it provides specific instructions for generating correlation IDs at entry points and passing them to all downstream services to create a complete request flow.

Observability Design

Name: Observability Design
Author: sethdford

bysethdford

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Analytics & Monitoring

Designs robust observability frameworks using metrics, logs, and traces to ensure production system visibility and rapid debugging.

This skill enables Claude to architect comprehensive observability strategies based on Google SRE principles and modern engineering standards. It guides developers in defining Service Level Indicators (SLIs), implementing distributed tracing with correlation IDs, and configuring efficient logging while avoiding common pitfalls like high cardinality. By integrating this skill, teams can transform opaque production environments into transparent systems, allowing for faster incident response, proactive performance monitoring, and detailed visibility across complex distributed architectures.

Key Features

01Defines key SLIs including success rates, p99 latency, and saturation

029 GitHub stars

03Architects low-cardinality metric collection for efficient storage and querying

04Implements distributed tracing patterns with correlation ID propagation

05Standardizes centralized logging strategies for critical events and error handling

06Develops actionable dashboard structures and alert runbooks for incident management

Use Cases

01Optimizing high-volume logging systems to reduce noise while increasing signal

02Designing a monitoring strategy for a new microservices architecture to improve reliability

03Debugging complex distributed system failures using structured trace correlations

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add sethdford/claude-skills observability-design

For use in Claude.ai and ChatGPT

Key Features

01Defines key SLIs including success rates, p99 latency, and saturation

029 GitHub stars

03Architects low-cardinality metric collection for efficient storage and querying

04Implements distributed tracing patterns with correlation ID propagation

05Standardizes centralized logging strategies for critical events and error handling

06Develops actionable dashboard structures and alert runbooks for incident management

Use Cases

01Optimizing high-volume logging systems to reduce noise while increasing signal

02Designing a monitoring strategy for a new microservices architecture to improve reliability

03Debugging complex distributed system failures using structured trace correlations